Alternative polyadenylation quantitative trait methylation mapping in human cancers provides clues into the molecular mechanisms of APA.

Genetic variants are involved in the orchestration of alternative polyadenylation (APA) events, while the role of DNA methylation in regulating APA remains unclear. We generated a comprehensive atlas of APA quantitative trait methylation sites (apaQTMs) across 21 different types of cancer (1,612 to 60,219 acting in cis and 4,448 to 142,349 in trans). Potential causal apaQTMs in non-cancer samples were also identified. Mechanistically, we observed a strong enrichment of cis-apaQTMs near polyadenylation sites (PASs) and both cis- and trans-apaQTMs in proximity to transcription factor (TF) binding regions. Through the integration of ChIP-signals and RNA-seq data from cell lines, we have identified several regulators of APA events, acting either directly or indirectly, implicating novel functions of some important genes, such as TCF7L2, which is known for its involvement in type 2 diabetes and cancers. Furthermore, we have identified a vast number of QTMs that share the same putative causal CpG sites with five different cancer types, underscoring the roles of QTMs, including apaQTMs, in the process of tumorigenesis. DNA methylation is extensively involved in the regulation of APA events in human cancers. In an attempt to elucidate the potential underlying molecular mechanisms of APA by DNA methylation, our study paves the way for subsequent experimental validations into the intricate biological functions of DNA methylation in APA regulation and the pathogenesis of human cancers. To present a comprehensive catalog of apaQTM patterns, we introduce the Pancan-apaQTM database, available at https://pancan-apaqtm-zju.shinyapps.io/pancanaQTM/.

BANMF-S: a blockwise accelerated non-negative matrix factorization framework with structural network constraints for single cell imputation.

MOTIVATION: Single cell RNA sequencing (scRNA-seq) technique enables the transcriptome profiling of hundreds to ten thousands of cells at the unprecedented individual level and provides new insights to study cell heterogeneity. However, its advantages are hampered by dropout events. To address this problem, we propose a Blockwise Accelerated Non-negative Matrix Factorization framework with Structural network constraints (BANMF-S) to impute those technical zeros. RESULTS: BANMF-S constructs a gene-gene similarity network to integrate prior information from the external PPI network by the Triadic Closure Principle and a cell-cell similarity network to capture the neighborhood structure and temporal information through a Minimum-Spanning Tree. By collaboratively employing these two networks as regularizations, BANMF-S encourages the coherence of similar gene and cell pairs in the latent space, enhancing the potential to recover the underlying features. Besides, BANMF-S adopts a blocklization strategy to solve the traditional NMF problem through distributed Stochastic Gradient Descent method in a parallel way to accelerate the optimization. Numerical experiments on simulations and real datasets verify that BANMF-S can improve the accuracy of downstream clustering and pseudo-trajectory inference, and its performance is superior to seven state-of-the-art algorithms. AVAILABILITY: All data used in this work are downloaded from publicly available data sources, and their corresponding accession numbers or source URLs are provided in Supplementary File Section 5.1 Dataset Information. The source codes are publicly available in Github repository https://github.com/jiayingzhao/BANMF-S.

ConSpaS: a contrastive learning framework for identifying spatial domains by integrating local and global similarities.

Spatial transcriptomics is a rapidly growing field that aims to comprehensively characterize tissue organization and architecture at single-cell or sub-cellular resolution using spatial information. Such techniques provide a solid foundation for the mechanistic understanding of many biological processes in both health and disease that cannot be obtained using traditional technologies. Several methods have been proposed to decipher the spatial context of spots in tissue using spatial information. However, when spatial information and gene expression profiles are integrated, most methods only consider the local similarity of spatial information. As they do not consider the global semantic structure, spatial domain identification methods encounter poor or over-smoothed clusters. We developed ConSpaS, a novel node representation learning framework that precisely deciphers spatial domains by integrating local and global similarities based on graph autoencoder (GAE) and contrastive learning (CL). The GAE effectively integrates spatial information using local similarity and gene expression profiles, thereby ensuring that cluster assignment is spatially continuous. To improve the characterization of the global similarity of gene expression data, we adopt CL to consider the global semantic information. We propose an augmentation-free mechanism to construct global positive samples and use a semi-easy sampling strategy to define negative samples. We validated ConSpaS on multiple tissue types and technology platforms by comparing it with existing typical methods. The experimental results confirmed that ConSpaS effectively improved the identification accuracy of spatial domains with biologically meaningful spatial patterns, and denoised gene expression data while maintaining the spatial expression pattern. Furthermore, our proposed method better depicted the spatial trajectory by integrating local and global similarities.

NG-SEM: an effective non-Gaussian structural equation modeling framework for gene regulatory network inference from single-cell RNA-seq data.

Inference of gene regulatory network (GRN) from gene expression profiles has been a central problem in systems biology and bioinformatics in the past decades. The tremendous emergency of single-cell RNA sequencing (scRNA-seq) data brings new opportunities and challenges for GRN inference: the extensive dropouts and complicated noise structure may also degrade the performance of contemporary gene regulatory models. Thus, there is an urgent need to develop more accurate methods for gene regulatory network inference in single-cell data while considering the noise structure at the same time. In this paper, we extend the traditional structural equation modeling (SEM) framework by considering a flexible noise modeling strategy, namely we use the Gaussian mixtures to approximate the complex stochastic nature of a biological system, since the Gaussian mixture framework can be arguably served as a universal approximation for any continuous distributions. The proposed non-Gaussian SEM framework is called NG-SEM, which can be optimized by iteratively performing Expectation-Maximization algorithm and weighted least-squares method. Moreover, the Akaike Information Criteria is adopted to select the number of components of the Gaussian mixture. To probe the accuracy and stability of our proposed method, we design a comprehensive variate of control experiments to systematically investigate the performance of NG-SEM under various conditions, including simulations and real biological data sets. Results on synthetic data demonstrate that this strategy can improve the performance of traditional Gaussian SEM model and results on real biological data sets verify that NG-SEM outperforms other five state-of-the-art methods.

Identification of potential functional variants and genes at 18q21.1 associated with the carcinogenesis of colorectal cancer.

Genome-wide association studies (GWAS) have identified more than 160 susceptibility loci for colorectal cancer (CRC). The effects of these variants, particularly their mechanisms, however, remain unclear. In this study, a comprehensive functional annotation of CRC-related GWAS signals was firstly conducted to identify the potential causal variants. We found that the SNP rs7229639 in intron 3 of SMAD7 at 18q21.1 might serve as a putative functional variant in CRC. The SNP rs7229639 is located in a region with evidence of regulatory potential. Dual-luciferase reporter assays revealed that three other SNPs (rs77544449, rs60385309 and rs72917785), in strong linkage disequilibrium (LD) with rs7229639, exhibited allele-specific enhancer activity, of which one of the target genes may conceivably be LIPG, as suggested by eQTL association data and Hi-C data. We also verified that LIPG promoted malignancy of CRC cells in vitro, with supporting clinical data indicating that LIPG is upregulated and correlated with a poor prognosis in CRC. Finally, pitavastatin was observed to exhibit an anti-CRC activity and modest inhibition of LIPG mRNA levels. Collectively, our data suggest that these functional variants at 18q21.1 are involved in the pathogenesis of CRC by modulating enhancer activity, and possibly LIPG expression, thus indicating a promising therapeutic target for CRC. The results of functional annotation in our investigation could also serve as an inventory for CRC susceptibility SNPs and offer guides for post-GWAS downstream functional studies.

Integrated analyses reveal the diagnostic and predictive values of COL5A2 and association with immune environment in Crohn's disease.

The pathogenesis of Crohn's disease (CD) involves abnormal immune cell infiltration and dysregulated immune response. Therefore, thorough research on immune cell abnormalities in CD is crucial for improved treatment of this disease. Single-cell RNA sequencing (scRNA-seq) and bulk RNA-seq data of CD were obtained from the Gene Expression Omnibus (GEO) database. Cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT), weighted gene co-expression network analysis (WGCNA), protein-protein interaction (PPI) networks evaluated the proportion of immune infiltrating cells, constructed co-expression network and identified key genes, respectively. Based on the dataset (GSE134809), 15 cell clusters were defined and labeled as different cell types. Among the 11 modules, the yellow module had the closest relationship with plasma cells (cluster 5). Confirmed using RNA sequencing and IHC assay, the expression of COL5A2 in CD samples was higher than that in control samples. Furthermore, the COL5A2 protein expression remarkably decreased in the group of patients who responded to anti-tumor necrosis factor (TNF) treatments, compared to the non-response group. The comprehensive analyses described here provided novel insight into the landscape of CD-associated immune environment. In addition, COL5A2 were identified as potential diagnostic indicators for CD, as well as promising predictive markers for CD patients.

Edge-Type Hyperintense Intracranial Artery Plaque: A Potential MRI Biomarker of Stroke Recurrence.

BACKGROUND: Identifying patients at high risk of stroke recurrence is important for stroke prevention and treatment. PURPOSE: To explore the characteristics of T1 hyperintense plaques (HIP) and their relationship with stroke recurrence in patients with symptomatic intracranial atherosclerotic stenosis (sICAS). STUDY TYPE: Retrospective. POPULATION: One hundred fifty-seven patients with moderate-to-severe (≥50%) nonocclusive sICAS and MRI studies (42 females and 115 males, mean age 58.69 ± 10.68 years). FIELD STRENGTH/SEQUENCE: 3D higher-resolution black-blood T1-weighted fast-spin-echo sequence at 3.0 T. ASSESSMENT: HIP (signal intensity [SI] of plaque-to-adjacent gray matter >1.0 on non-contrast T1-weighted images) and non-HIP plaques were identified. HIP plaques were categorized as edge type (high SI adjacent to lumen) and non-edge type (high SI within plaque). Clinical and imaging features of different plaque types were compared. Stroke recurrence was assessed through telephone or medical records at 3 and 6 months, and then once a year post-MRI. The relationship between edge type and non-edge types HIP with stroke recurrence was analyzed. STATISTICAL TESTS: Student's t test, Mann-Whitney U-test, chi square test and Fisher's exact test to compare features between plaque types. Kaplan-Meier curves (with log-rank tests) and Cox proportional hazards regression to assess relationship between stroke recurrence and different plaque types. A two-tailed P-value of <0.05 was considered statistically significant. RESULTS: Of 157 culprit lesions, 87 (55%) were HIPs (43 edge type, 44 non-edge type) and 70 (45%) were non-HIPs. Plaque thickness, area, and volume were significantly higher for HIPs than for non-HIPs. Among patients with HIPs, edge type was significantly more likely in the posterior circulation (53.5% vs. 27.3%), and had significantly higher plaque thickness, length, area, volume, plaque burden, and remodeling index than non-edge type. Edge-type HIP was significantly more common than non-edge HIP in patients with diabetes mellitus (51.2% vs. 29.5%) and dyslipidemia (79.1% vs. 54.5%). During median follow-up of 27 months, 33 patients experienced stroke recurrence. Recurrence was associated with edge-type HIP (adjusted hazard ratio = 2.83; 95% confidence interval: 1.40-5.69), both in the overall cohort (34.9% vs. 15.8%) and in patients with HIP (34.9% vs. 9.0%). Age ≥60 years and edge-type HIP had a significant interaction. DATA CONCLUSIONS: Hyperintense plaque may be categorized as edge type or non-edge type. Edge-type HIP may be a potential MRI biomarker of stroke recurrence. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 2.

Distinguishing Intracranial Diabetes-Related Atherosclerotic Plaques: A High-Resolution Magnetic Resonance Imaging-Based Radiomics Study.

INTRODUCTION: Diabetes markedly affects the formation and development of intracranial atherosclerosis. The study was aimed at evaluating whether radiomics features can help distinguish plaques primarily associated with diabetes. MATERIALS AND METHODS: We retrospectively analyzed patients who were admitted to our center because of acute ischemic stroke due to intracranial atherosclerosis between 2016 and 2022. Clinical data, blood biomarkers, conventional plaque features, and plaque radiomics features were collected for all patients. Odds ratios (ORs) with 95% confidence intervals (CIs) were determined from logistic regression models. The receiver operating characteristic (ROC) curve and area under the ROC curve (AUC) were used to describe diagnostic performance. The DeLong test was used to compare differences between models. RESULTS: Overall, 157 patients (115 men; mean age, 58.7 ± 10.7 years) were enrolled. Multivariate logistic regression analysis showed that plaque length (OR: 1.17; 95% CI: 1.07-1.28) and area (OR: 1.13; 95% CI: 1.02-1.24) were independently associated with diabetes. On combining plaque length and area as a conventional model, the AUCs of the training and validation cohorts for identifying diabetes patients were 0.789 and 0.720, respectively. On combining radiomics features on T1WI and contrast-enhanced T1WI sequences, a better diagnostic value was obtained in the training and validation cohorts (AUC: 0.889 and 0.861). The DeLong test showed the model combining radiomics and conventional plaque features performed better than the conventional model in both cohorts (p < 0.05). CONCLUSIONS: The use of radiomics features of intracranial plaques on high-resolution magnetic resonance imaging can effectively distinguish culprit plaques with diabetes as the primary pathological cause, which will provide new avenues of research into plaque formation and precise treatment.

Loss of GLTSCR1 causes congenital heart defects by regulating NPPA transcription.

Precise and specific spatiotemporal domains of gene expression regulation are critical for embryonic development. Recent studies have identified GLTSCR1 as a gene transcriptional elongation regulator in cancer research. However, the function of GLTSCR1, especially in embryonic development, remains poorly understood. Here, we found that GLTSCR1 was essential for cardiac development because Gltscr1 knockout (Gltscr1-/-) led to embryonic lethality in mice with severe congenital heart defects (CHDs). Ventricular septal defect and double outflow right ventricular were also observed in neural crest cells with conditional deletion of Gltscr1, which were associated with neonatal lethality in mice. Mechanistically, GLTSCR1 deletion promoted NPPA expression by coordinating the CHD risk G allele of rs56153133 in the NPPA enhancer and releasing the transcription factor ZNF740-binding site on the NPPA promoter. These findings demonstrated that GLTSCR1 acts as a candidate CHD-related gene.

Paeoniflorin, ferulic acid, and atractylenolide III improved LPS-induced neuroinflammation of BV2 microglia cells by enhancing autophagy.

Microglia hyperactivation is an important cause of neuroinflammation in Alzheimer's disease (AD). Paeoniflorin (PF), ferulic acid (FA), and atractylenolide III (ATL) are potent in anti-inflammation and neuroprotection. Multiple components can act on different targets simultaneously to exert synergistic therapeutic effects and exploring the synergistic potential between compounds is an important area of research. We investigated the effects of PF, FA, and ATL, alone or in combination, on LPS-induced neuroinflammation and autophagy in BV2 microglia cells. We found that PF, FA, and ATL, alone or in combination, significantly reduced the production of inflammatory factors such as IL-6, IL-1ß, and TNF-α, especially in the PF + FA + ATL group, which performed the best. In addition, the combination of PF, FA, and ATL significantly increased the expression of autophagy-related proteins p-AMPK, p-ULK1, Beclin1, LC3, and TFEB and decreased the expression of p62. Moreover, the restoration of autophagic flux by the combination of PF, FA, and ATL was abrogated by the addition of the autophagy inhibitor Wortmannin. In conclusion, PF, FA, and ATL have a synergistic effect in reducing LPS-induced inflammatory factor release from BV2 microglia cells, and its protective effect may be through activation of the AMPK/ULK1/TFEB autophagic signaling pathway.

Protective Effect of Ferulic Acid on Lipopolysaccharide-Induced BV2 Microglia Inflammation via AMPK/mTOR Signaling Pathway.

In neurodegenerative diseases, microglial activation and neuroinflammation are essential for the control and progression of neurodegenerative diseases. Mitigating microglium-induced inflammation is one strategy for hindering the progression of neurodegenerative diseases. Ferulic acid (FA) is an effective anti-inflammatory agent, but its potential role and regulation mechanism in neuroinflammatory reactions have not been fully studied. In this study, the neuroinflammation model was established by lipopolysaccharide (LPS), and the inhibitory effect of FA on neuroinflammation of BV2 microglia was studied. The results showed that FA significantly reduced the production and expression of reactive oxygen species (ROS), tumor necrosis factor-α (TNF-α), leukocyte-6 (IL-6) and interleukin-1ß (IL-1ß). We further studied the mechanism of FA's regulation of LPS-induced BV2 neuroinflammation and found that FA can significantly reduce the expression of mTOR in BV2 microglia induced by LPS, and significantly increase the expression of AMPK, indicating that FA may have an anti-inflammatory effect by activating the AMPK/mTOR signaling pathway to regulate the release of inflammatory mediators (such as NLRP3, caspase-1 p20 and IL-1ß). We further added an autophagy inhibitor (3-MA) and an AMPK inhibitor (compound C, CC) for reverse verification. The results showed that FA's inhibitory effects on TNF-α, IL-6 and IL-1ß and its regulatory effect on AMPK/mTOR were destroyed by 3-MA and CC, which further indicated that FA's inhibitory effect on neuroinflammation is related to its activation of the AMPK/mTOR autophagy signaling pathway. In a word, our experimental results show that FA can inhibit LPS-induced neuroinflammation of BV2 microglia by activating the AMPK/mTOR signaling pathway, and FA may be a potential drug for treating neuroinflammatory diseases.

Review of net water uptake in the management of acute ischemic stroke.

CT densitometry-based methods to directly quantify net water uptake in ischemic brain tissue have been increasingly applied recently. There is potential for net water uptake to be used as an imaging biomarker for the pathophysiology of infarcted lesions. This review is aimed at summarizing the potential and current status of the application of net water uptake as a biomarker in the management of ischemic stroke and future directions in this context. Specifically, we provide a brief overview of the principle and different methods of net water uptake measurement, followed by a discussion of the role of net water uptake in predicting malignant brain edema and hemorrhagic transformation, evaluating lesion age, and predicting the efficacy of reperfusion therapy and long-term clinical prognosis. Artificial intelligence will help address the lack of automation and standardization in the measurement of net water absorption. Further validation of net water uptake in a prospective multicenter setting is necessary. KEY POINTS: • NWU can be used as a quantitative imaging biomarker for developing malignant brain edema in anterior and posterior circulation strokes. • The difference in NWU in edema arrest or reversibility suggests that rapid and successful revascularization can influence the progression of ischemic edema. • NWU can be used to predict the age of a lesion, with predictive power comparable to that of DWI/FLAIR mismatch.

Automated quantitative lesion water uptake in acute stroke is a predictor of malignant cerebral edema.

OBJECTIVES: Net water uptake (NWU) has been shown to have a linear relationship with brain edema. Based on an automated-Alberta Stroke Program Early Computed Tomography Score (ASPECTS) technique, we automatically derived NWU from baseline multimodal computed tomography (CT), namely ASPECTS-NWU. We aimed to determine if ASPECTS-NWU can predict the development of malignant cerebral edema (MCE). METHODS: One hundred and forty-six patients with large-vessel occlusion were retrospectively enrolled. Quantitative NWU based on automated-ASPECTS was measured both on nonenhanced CT (NECT) and CT angiography (CTA), namely NECT-ASPECT-NWU and CTA-ASPECTS-NWU. The correlation between ASPECTS-NWU and cerebral edema (CED) grades was calculated using Spearman rank correlation. Univariate logistic regression was used to assess the effect of radiological and clinical features on MCE, and a multivariable model with significant factors from the univariate regression analysis was built. Receiver operating characteristic (ROC) was obtained and area under curve (AUC) was compared. RESULTS: CTA-ASPECTS-NWU had a moderate positive correlation with CED grades (r = 0.62; 95% confidence interval [CI], 0.51-0.71; p < 0.001). The CTA-ASPECTS-NWU performed better than the NECT-ASPECTS-NWU with AUC: 0.88 vs. 0.71 (p < 0.001). Multivariable logistic regression model integrating CTA-ASPECTS-NWU, collateral score, and age showed the CTA-ASPECTS-NWU was an independent predictor of MCE with an AUC of 0.94 (95% CI: 0.90-0.98; p < 0.001). CONCLUSIONS: This study demonstrates that ASPECTS-NWU is a quantitative predictor of MCE after large-vessel occlusion of the middle cerebral artery territory. The multivariable logistic regression model may enhance the identification of patients with MCE needing anti-edematous treatment. KEY POINTS: • The automated-ASPECTS technique can automatically detect the affected regions with early ischemic changes and NWU could be manually calculated. • The CTA-ASPECTS-NWU performs better than the NECT-ASPECTS-NWU on predicting the development of MCE. • The multivariable logistic regression model may enhance the identification of patients with MCE needing anti-edematous treatment.

The MRI enhancement ratio and plaque steepness may be more accurate for predicting recurrent ischemic cerebrovascular events in patients with intracranial atherosclerosis.

OBJECTIVES: To assess the complementary value of high-resolution multi-contrast MRI (hrMRI) in identifying symptomatic patients with intracranial atherosclerosis (ICAS) who are likely to experience recurrent ischemic cerebrovascular events. METHODS: In this retrospective cohort study, eighty patients with acute ischemic events attributed to ICAS who underwent hrMRI examination between January 2015 and January 2019 were included. Median follow-up for all patients was 30 months (range: 1 to 52 months) and recurrent ischemic cerebrovascular events were recorded. Cox regression analysis and time-dependent ROC were performed to quantify the association between the plaque characteristics and recurrent events. RESULTS: During the follow-up, 14 patients experienced recurrent ischemic cerebrovascular events. Young males and those with diabetes and poor medication persistence were more likely to experience recurrent events. ICAS in patients with recurrence had significantly higher enhancement ratio and steepness which is defined as the ratio between the plaque height and length than those without (p < 0.001 and p = 0.015, respectively). After adjustment of clinical factors, enhancement ratio (HR, 13.13 [95% CI, 3.58-48.20], p < 0.001) and plaque steepness (HR, 110.27 [95% CI, 4.75-2560.91], p = 0.003) were independent imaging biomarkers associated with recurrent events. Time-dependent ROC indicated that integrated high enhancement ratio and steepness into clinical risk factors improved discrimination power with the ROC increased from 0.79 to 0.94 (p = 0.008). CONCLUSIONS: The enhancement ratio and plaque steepness improved the accuracy over traditional clinical risk factors in predicting recurrent ischemic cerebrovascular events for patients with ICAS. KEY POINTS: • High-resolution magnetic resonance imaging helps clinicians to evaluate high-risk Intracranial plaque. • The higher enhancement ratio and plaque steepness (= height/length) were the primary biomarkers associated with future ischemic cerebrovascular events. • High-resolution magnetic resonance imaging combined with clinical characteristics showed a higher accuracy for the prediction of recurrent events in patients with intracranial atherosclerosis.

Radiomics versus Conventional Assessment to Identify Symptomatic Participants at Carotid Computed Tomography Angiography.

INTRODUCTION: Carotid computed tomography angiography (CTA) is routinely used for evaluating the atherosclerotic process. Radiomics allows the extraction of imaging markers of lesion heterogeneity and spatial complexity. These quantitative features can be used as the input for machine learning (ML). Therefore, in this study, we aimed to evaluate the diagnostic performance of radiomics-based ML assessment of carotid CTA data to identify symptomatic patients with carotid artery atherosclerosis. METHODS: In this retrospective study, participants with carotid artery atherosclerosis who underwent carotid CTA and brain magnetic resonance imaging from May 2010 to December 2017 were studied. The participants were grouped into symptomatic and asymptomatic groups according to their recent symptoms (determination of ipsilateral ischemic stroke). Eight conventional plaque features and 2,107 radiomics parameters were extracted from carotid CTA images. A radiomics-based ML model was fitted on the training set, and the radiomics-based ML model and conventional assessment were compared using the area under the curve (AUC) to identify symptomatic participants. RESULTS: After excluding participants with other stroke sources, 120 patients with 148 carotid arteries were analyzed. Of these 148 carotid arteries, 34 (22.97%) were classified into the symptomatic group. Plaque ulceration (odds ratio [OR] = 0.257; 95% confidence interval [CI], 0.094-0.698) and plaque enhancement (OR = 0.305; 95% CI, 0.094-0.988) were associated with the symptomatic status. Twenty radiomics parameters were chosen to be inputs in the radiomics-based ML model. In the identification of symptomatic participants, the discriminatory value of the radiomics-based ML model was significantly higher than that of the conventional assessment (AUC = 0.858 vs. AUC = 0.706, p = 0.021). CONCLUSION: Radiomics-based ML analysis improves the discriminatory power of carotid CTA in the identification of recent ischemic symptoms in patients with carotid artery atherosclerosis.

ASPECTS-Based Attenuation Changes on CT Angiography as an Imaging Biomarker to Predict Hemorrhagic Transformation in Acute Ischemic Stroke.

INTRODUCTION: Imaging-based early warning indicators and feasible stratification of acute ischemic stroke (AIS) patients with hemorrhagic transformation (HT), especially high-risk patients with parenchymal hematoma (PH), are crucial in determining subsequent treatment strategies. This study combined automated ASPECTS software with noncontrast CT (NCCT) and CTA source image (CTASI) attenuation changes using Hounsfield unit (HU) values to predict HT and PH in patients with AIS. MATERIALS AND METHODS: We retrospectively enrolled 172 consecutive patients with anterior circulation large-vessel occlusion between 2016 and 2020. Univariate and multivariate logistic regression and receiver operating characteristic (ROC) analyses were used to investigate the relationship between NCCT and CTASI-ASPECTS-HU, as well as other clinical and radiological parameters of HT and PH. Univariate and multivariate logistic regression analyses were performed to explore risk factors for HT or PH, and an ROC curve was used to evaluate their diagnostic values. RESULTS: A multivariate analysis showed that CTASI-ASPECTS-HU and NIHSS score were independent predictors of HT (CTASI-ASPECTS-HU: odds ratio (OR), 2.22; 95% CI, 1.01-4.92; NIHSS: OR, 1.07; 95% CI, 1.02-1.13) and PH (CTASI-ASPECTS-HU: OR, 6.51; 95% CI, 2.29-18.50; NIHSS: OR, 1.07; 95% CI, 1.01-1.13). According to ROC analysis, CTASI-ASPECTS-HU >0.09 identified HT (area under the curve, 0.70; sensitivity, 70.15%; specificity, 61.90%), and CTASI-ASPECTS-HU >0.10 identified PH (area under the curve, 0.79; sensitivity, 76.19%; specificity, 73.33%). The area under the curve for predicting HT or PH increased when CTASI-ASPECTS-HU was combined with NIHSS score (HT: area under the curve, 0.74; sensitivity, 73.13%; specificity, 70.48%; PH: area under the curve, 0.81; sensitivity, 85.71%; specificity, 72.38%). CONCLUSION: CTASI-ASPECTS-HU is a reliable radiological predictor of HT and PH in patients with AIS. Its predictive efficacy is moderately improved when combined with NIHSS score.

Predicting incidence of hepatitis E using machine learning in Jiangsu Province, China.

Hepatitis E is an increasingly serious worldwide public health problem that has attracted extensive attention. It is necessary to accurately predict the incidence of hepatitis E to better plan ahead for future medical care. In this study, we developed a Bi-LSTM model that incorporated meteorological factors to predict the prevalence of hepatitis E. The hepatitis E data used in this study are collected from January 2005 to March 2017 by Jiangsu Provincial Center for Disease Control and Prevention. ARIMA, GBDT, SVM, LSTM and Bi-LSTM models are adopted in this study. The data from January 2009 to September 2014 are used as the training set to fit models, and data from October 2014 to March 2017 are used as the testing set to evaluate the predicting accuracy of different models. Selecting models and evaluating the effectiveness of the models are based on mean absolute per cent error (MAPE), root mean square error (RMSE) and mean absolute error (MAE). A total of 44 923 cases of hepatitis E are detected in Jiangsu Province from January 2005 to March 2017. The average monthly incidence rate is 0.35 per 100 000 persons in Jiangsu Province. Incorporating meteorological factors of temperature, water vapour pressure, and rainfall as a combination into the Bi-LSTM Model achieved the state-of-the-art performance in predicting the monthly incidence of hepatitis E, in which RMSE is 0.044, MAPE is 11.88%, and MAE is 0.0377. The Bi-LSTM model with the meteorological factors of temperature, water vapour pressure, and rainfall can fully extract the linear and non-linear information in the hepatitis E incidence data, and has significantly improved the interpretability, learning ability, generalisability and prediction accuracy.

Comparison of automated and manual DWI-ASPECTS in acute ischemic stroke: total and region-specific assessment.

OBJECTIVE: To compare the DWI-Alberta Stroke Program Early Computed Tomography Score calculated by a deep learning-based automatic software tool (eDWI-ASPECTS) with the neuroradiologists' evaluation for the acute stroke, with emphasis on its performance on 10 individual ASPECTS regions, and to determine the reasons for inconsistencies between eDWI-ASPECTS and neuroradiologists' evaluation. METHODS: This retrospective study included patients with middle cerebral artery stroke who underwent MRI from 2010 to 2019. All scans were evaluated by eDWI-ASPECTS and two independent neuroradiologists (with 15 and 5 years of experience in stroke study). Inter-rater agreement and agreement between manual vs. automated methods for total and each region were evaluated by calculating Kendall's tau-b, intraclass correlation coefficient (ICC), and kappa coefficient. RESULTS: In total, 309 patients met our study criteria. For total ASPECTS, eDWI-ASPECTS and manual raters had a strong positive correlation (Kendall's tau-b = 0.827 for junior raters vs. eDWI-ASPECTS; Kendall's tau-b = 0.870 for inter-raters; Kendall's tau-b = 0.848 for senior raters vs. eDWI-ASPECTS) and excellent agreement (ICC = 0.923 for junior raters and automated scores; ICC = 0.954 for inter-raters; ICC = 0.939 for senior raters and automated scores). Agreement was different for individual ASPECTS regions. All regions except for M5 region (κ = 0.216 for junior raters and automated scores), internal capsule (κ = 0.525 for junior raters and automated scores), and caudate (κ = 0.586 for senior raters and automated scores) showed good to excellent concordance. CONCLUSION: The eDWI-ASPECTS performed equally well as senior neuroradiologists' evaluation, although interference by uncertain scoring rules and midline shift resulted in poor to moderate consistency in the M5, internal capsule, and caudate nucleus regions. KEY POINTS: • The eDWI-ASPECTS based on deep learning perform equally well as senior neuroradiologists' evaluations. • Among the individual ASPECTS regions, the M5, internal capsule, and caudate regions mainly affected the overall consistency. • Uncertain scoring rules and midline shift are the main reasons for regional inconsistency.

Integrated multi-omics data analyses for exploring the co-occurring and mutually exclusive gene alteration events in colorectal cancer.

Co-occurring and mutually exclusive gene alteration events are helpful for understanding carcinogenesis but systematic screening for such events is quite limited. We conducted pairwise screening tests to identify "hit pairs" in colorectal cancer (CRC) by utilizing the cross-omics data from The Cancer Genome Atlas (TCGA). Numerous hit pairs involving somatic mutations, copy number variations, and DNA methylation were found to occur nonrandomly in CRC, such as KRAS and HOXB6, SMAD4 and PMEPA1. Based on these hit pairs, we identified 32 synthetic lethal pairs and 7,527 co-occurring pairs relating to drug response. Our further biological experiments showed that the co-occurrence of mutant FCGBP and NUDT12 silencing (or mutant TMC3 and RPS6KA6 silencing) with small interfering RNA reduced cell viability. Moreover, novel hit pairs could influence prognosis. The patients who carried concurrent mutations of IRF5 and NEFH, SYNE1 and TTN, or MUC16 and NEFH had worse survival outcomes. Particularly, the presence of mutant SYNE1 and TTN pair not only affects prognosis, but also is related to CRC patients' response to drug treatment. Our "hit pair" genes may provide insights into colorectal carcinogenesis and help open new avenues for CRC therapy.

Reliability and Reproducibility of the Diameters Method in Rapid Determination of Acute Infarct Volume in Magnetic Resonance Diffusion-Weighted Imaging.

BACKGROUND: In acute ischemic stroke, diffusion-weighted imaging (DWI) volume is an independent predictive factor of poor outcome and an exclusion criterion for thrombolytic treatment. A simplified diameters method (ABC/2, orthogonal diameter [OD], and the maximum diameter [MD]) was proposed to replace the conventional measuring method and overcome the tedious and time-consuming defects, but its accuracy remains to be determined. OBJECTIVE: The objective of this study is to clarify the reliability and reproducibility of the diameter-based estimations in the infarct volume in DWI (Vol-DWI) measured by automated software. METHODS: Data of 316 patients with acute ischemic stroke who underwent MRI within 72 h at Jinling Hospital were retrospectively reviewed. Subgroup analysis by the location (cortex, white matter and deep gray nuclei, and combined) and volume (<70 and >70 mL) of cerebral infarction was evaluated. Relationship and consistency between the diameters methods and Vol-DWI were determined using Spearman rank correlation, Wilcoxon signed-rank test, and Bland-Altman plots. The OD and MD thresholds indicating infarct size >15, 70, and 100 mL were determined by generating receiver-operating characteristic (ROC) curves. Interobserver reliability was established using intraclass correlation coefficient and Bland-Altman plot. RESULTS: There was a strong positive correlation between the diameters and the Vol-DWI (ABC/2: r = 0.992, OD: r = 0.984, MD: r = 0.970, p < 0.001). Infarct volumes measured using the ABC/2 formula were significantly lower than those measured with Vol-DWI (Wilcoxon signed-rank test, z = 6.476, p < 0.001). Bland-Altman plot showed that the agreement of the volume <70 mL group, and white matter and deep gray nuclei groups was better than that of the other subgroups. For infarct volumes >15, 70, and 100 mL, the cutoff value for the MD was identified at 5, 6.9, and 8.4 cm, and the OD was identified at 12.47, 26.4, and 36.4 cm2, respectively, with a sensitivity and specificity >90%. CONCLUSIONS: The MD method was the best for achieving a rapid and excellent interobserver reliability for estimating infarct volume. Both OD and MD methods can quickly screen patients suitable for recanalization treatment and predict poor prognosis through threshold evaluation.